Interaction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study

IntroductionThe current understanding of colorectal carcinogenesis is based on the adenoma-carcinoma sequence, where genetics, intestinal microbiota changes and local immunity shifts seem to play the key roles. Despite the emerging evidence of dysbiotic intestinal state and immune-cell infiltration changes in patients with colorectal adenocarcinoma, early and advanced adenoma as precursors of colorectal cancer, and carcinoma in situ as the following progression, are rather less studied. The newly colon-site adapted AI-based analysis of immune infiltrates is able to predict long-term outcomes of colon carcinoma. Though it could also facilitate the pathologic evaluation of precancerous lesion’s potential to progress. Therefore, the purpose of this prospective cohort study (MIMICA-1) is, firstly, to identify the intestinal microbiota and immune infiltration patterns around the normal bowel tissue, early and advanced adenoma, carcinoma in situ, and adenocarcinoma, and secondly, to analyze the immune – microbiome interplay along the steps of conventional colorectal tumorigenesis.Methods and analysesThis study aims to prospectively recruit 40 patients (10 per group) with confirmed colorectal dysplasia undergoing endoscopic polypectomy, endoscopic mucosal resection for colorectal small (≤1cm), and large (>1cm) adenoma or carcinoma in situ, or biopsy and subsequent colon resection for invasive colorectal cancer, and 10 healthy patients undergoing screening colonoscopy. Stool samples will be collected prior to bowel preparation for the analysis of fecal (luminal) microbiota composition. Biopsy specimens will be taken from the terminal ileum, right colon, left colon, and a pathological lesion in the colon (if present) to assess mucosa-associated microbiota composition and intestinal immunity response. DNA will be extracted from all samples and sequenced using the Illumina MiSeq platform. Unifrac and Bray-Curtis methods will be used to assess microbial diversity. The intestinal immune system response will be examined using digital image analysis where primarily immunohistochemistry procedures for CD3, CD8, CD20 and CD68 immune cell markers will be performed. Thereafter, the count, density and distribution of immunocompetent cells in epithelial and stromal tissue compartments will be evaluated using AI-based platform. The interaction between the microbial shifts and intestinal immune system response in adenoma-carcinoma sequence and the healthy patients will be examined. In addition, fecal samples will be explored for gut microbiota’s composition, comparing fecal- and tissue-derived bacterial patterns in healthy gut and along the adenoma-carcinoma sequence.DiscussionWe hypothesize that changes within the human gut microbiota led to detectable alterations of the local immune response and correlate with the progression from normal mucosa to colorectal adenoma and invasive carcinoma. It is expectable to find more severe gut immune infiltration at dysplasia site, though analyzing invasive colorectal cancer we expect to detect broader mucosa-associated and luminal microbiota changes with subsequent local immune response at near-lesion site and possibly throughout the entire colon. We believe that specific compositional differences detected around premalignant colorectal lesions are critically important for its primary role in initiation and acceleration of colorectal carcinogenesis. Thus, these microbial patterns could potentially supplement fecal immunohistochemical tests for the early non-invasive detection of colorectal adenoma. Moreover, AI-based analysis of immune infiltrates could become additional diagnostic and prognostic tool in precancerous lesions prior to the development of colorectal cancer.RegistrationThe study is registered at the Australian New Zealand Clinical Trials Registry (ACTRN12624000976583) https://www.anzctr.org.au/

Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer

Background: Gene expression studies have identified molecular subtypes of breast cancer with implications to chemotherapy recommendations. For distinction of these types, a combination of immunohistochemistry (IHC) markers, including proliferative activity of tumor cells, estimated by Ki67 labeling index is used. Clinical studies are frequently based on IHC performed on tissue microarrays (TMA) with variable tissue sampling. This raises the need for evidence-based sampling criteria for individual IHC biomarker studies. We present a novel tissue sampling simulation model and demonstrate its application on Ki67 assessment in breast cancer tissue taking intratumoral heterogeneity into account.Methods: Whole slide images (WSI) of 297 breast cancer sections, immunohistochemically stained for Ki67, were subjected to digital image analysis (DIA). Percentage of tumor cells stained for Ki67 was computed for hexagonal tiles super-imposed on the WSI. From this, intratumoral Ki67 heterogeneity indicators (Haralick’s entropy values) were extracted and used to dichotomize the tumors into homogeneous and heterogeneous subsets. Simulations with random selection of hexagons, equivalent to 0.75 mm circular diameter TMA cores, were performed. The tissue sampling requirements were investigated in relation to tumor heterogeneity using linear regression and extended error analysis.Results: The sampling requirements were dependent on the heterogeneity of the biomarker expression. To achieve a coefficient error of 10 %, 5–6 cores were needed for homogeneous cases, 11–12 cores for heterogeneous cases; in mixed tumor population 8 TMA cores were required. Similarly, to achieve the same accuracy, approximately 4,000 nuclei must be counted when the intratumor heterogeneity is mixed/unknown. Tumors of low proliferative activity would require larger sampling (10–12 TMA cores, or 6,250 nuclei) to achieve the same error measurement results as for highly proliferative tumors.Conclusions: Our data show that optimal tissue sampling for IHC biomarker evaluation is dependent on the heterogeneity of the tissue under study and needs to be determined on a per use basis. We propose a method that can be applied to determine the sampling strategy for specific biomarkers, tissues and study targets. In addition, our findings highlight the benefit of high-capacity computer-based IHC measurement techniques to improve accuracy of the testing

Bimodality of intratumor Ki67 expression is an independent prognostic factor of overall survival in patients with invasive breast carcinoma

Proliferative activity, assessed by Ki67 immunohistochemistry (IHC), is an established prognostic and predictive biomarker of breast cancer (BC). However, it remains under-utilized due to lack of standardized robust measurement methodologies and significant intratumor heterogeneity of expression. A recently proposed methodology for IHC biomarker assessment in whole slide images (WSI), based on systematic subsampling of tissue information extracted by digital image analysis (DIA) into hexagonal tiling arrays, enables computation of a comprehensive set of Ki67 indicators, including intratumor variability. In this study, the tiling methodology was applied to assess Ki67 expression in WSI of 152 surgically removed Ki67-stained (on full-face sections) BC specimens and to test which, if any, Ki67 indicators can predict overall survival (OS). Visual Ki67 IHC estimates and conventional clinico-pathologic parameters were also included in the study. Analysis revealed linearly independent intrinsic factors of the Ki67 IHC variance: proliferation (level of expression), disordered texture (entropy), tumor size and Nottingham Prognostic Index, bimodality, and correlation. All visual and DIA-generated indicators of the level of Ki67 expression provided significant cutoff values as single predictors of OS. However, only bimodality indicators (Ashman’s D, in particular) were independent predictors of OS in the context of hormone receptor and HER2 status. From this, we conclude that spatial heterogeneity of proliferative tumor activity, measured by DIA of Ki67 IHC expression and analyzed by the hexagonal tiling approach, can serve as an independent prognostic indicator of OS in BC patients that outperforms the prognostic power of the level of proliferative activity

A set of Ki67 intratumoral heterogeneity and CD8 immunogradient indicators

Šie duomenys skirti parametrų rinkiniui apskaičiuoti, naudotam straipsnyje „Spatial distributions of CD8 and Ki67 cells in the tumor microenvironment independently predict breast cancer-specific survival in patients with ER+HER2– and triple-negative breast carcinoma,” paskelbtame PLOS ONE 2024 m.These data are intended for calculating the parameter set used in the article "Spatial distributions of CD8 and Ki67 cells in the tumor microenvironment independently predict breast cancer-specific survival in patients with ER+HER2– and triple-negative breast carcinoma," published in PLOS ONE in 2024

Immunogradient

Metodas skirtas automatiniam naviko ribos nustatymui panaudojant skaitmeninės vaizdo analizės klasifikatoriaus rezultatusA method of Tumour-Stroma interface zone detection from immunogradient indicators in cancer patients, that can be used to determine the prognosis of patient survival

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies

Abstract Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment

Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies.

Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment

Intratumoral heterogeneity and immune response indicators to predict overall survival in a retrospective study of HER2-borderline (IHC 2+) breast cancer patients

Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients